Limitations of Row-and-Column Database Systems

Allow me to review four
reasons why rows and columns are as inappropriate in a modern IT
environment as bringing an Apple II computer to work.

Reason No. 1: Rows and columns are not scalable for modern data volumes

As the amount of data in the
enterprise continues to double each year, so do the sizes and numbers
of tables. As the tables get larger, queries from analytics
applications must scan through an increasing number of rows and columns
to find the selected data. If current trends continue, this will become
utterly crippling for IT in this decade because tables will be too
large to search despite advances in hardware performance. These same
trends will also make tables incredibly burdensome to manage.

Reason No. 2: Tables are a full-time job

Today we've come to accept
that large enterprises need a team of IT staff managing tables-creating
them, loading them, joining them, reading them into memory, scanning
them, sorting them, storing them and
reorganizing them. All this table housekeeping is becoming increasingly
burdensome for three reasons.

First, large volumes of
unstructured data must be loaded into tables and indexed. Second, as
data volumes grow, so do the number of tables that must be maintained.
And third, performance requirements push IT to constantly, manually
structure and restructure tables to achieve acceptable performance.

Reason No. 3: Rows and columns were never designed for analytics

The row-and-column format was
created before computerized business analytics existed and worked well
for transaction processing. Information stored in rows and columns is
not inherently useful for analytics and must be indexed. This indexing
process creates delays between when data is ingested and when it will
become available for query.

Reason No. 4: Rows and columns are a rigid static structure

Row-and-column-based
databases are built for specific applications, often before IT knows
how those applications will be used. Then, as usage patterns change and
the business needs to look at its data differently, the entire table
structure must be manually optimized to achieve acceptable query
performance. The next-generation of advanced analytics will not be
possible with rigid, static row-and-column structures because of the
manual overhead of incorporating unstructured data or enabling ad hoc
analytic queries.

Charles H. Silver is CEO at Algebraix Data Corporation. Charles has more than 25 years of experience as a successful entrepreneur. Most recently, he sold new media company RealAge Inc. to Hearst Corporation in 2007. Charles founded RealAge in the late 1990s based on a ground-breaking business plan for building revenue and attracting customers. Prior to his nine years at RealAge, Charles built a series of profitable franchises in the retail and real estate markets. After graduating from the University of Michigan in 1981, Charles spent the first few years of his career as a staffer for the governor of Michigan and for a U.S. Congressman. He can be reached at CSilver@algebraixdata.com.